CAI Orchestrator for Virtual Agent Communication
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Solution Overview
Problem
Existing virtual agent systems face scalability and modularity issues when handling multiple skills and data types, leading to increased testing and development time, synchronization of release schedules, and data privacy concerns, while separate instances cannot communicate during a user session, affecting user experience.
Innovation Solution
A Conversational Artificial Intelligence (CAI) orchestrator manages communication between multiple virtual agents, determining user intent and routing requests and responses seamlessly across agents, enabling independent development and use of multiple virtual agents within a single session without interrupting user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate virtual agent instances are used to handle multiple skills, then data privacy and independent development are improved, but communication between agents during user sessions is lost and user experience deteriorates
Solution Approach 1:
An orchestrator component is introduced as an intermediary between separate virtual agent instances. The orchestrator manages communication and coordination between agents during user sessions, enabling data transfer and seamless interaction while preserving the independence and data privacy benefits of separate agent instances.
2Ease of manufacture
If multiple separate virtual agent instances are deployed, then independent development and skills modularity are improved, but testing and development time increases due to synchronization requirements
Solution Approach 1:
The virtual agent system is segmented into independent, modular instances, each capable of being developed and tested separately. The orchestrator coordinates these segmented agents, allowing parallel development without requiring synchronization of release schedules, thereby reducing overall testing and development time.
3Device complexity
If a single virtual agent instance handles multiple skills, then development coordination is simplified, but scalability and modularity are reduced
Solution Approach 1:
The orchestrator provides universal coordination functionality that enables multiple specialized virtual agent instances to work together seamlessly. This allows the system to maintain simplicity in coordination (through the universal orchestrator) while achieving high scalability and modularity (through specialized independent agents).
Data Source
AI summary
Techniques are disclosed that relate to a computer system implementing an interactive voice response (IVR) transcoder. The computer system may receive voice input from an interactive voice response (IVR) system, or other channel. The computer system converts the received input to a request having a common format supported by an artificial intelligence (AI) core including one or more virtual agents. The computer system routes the request to the AI core operable to handle requests specified in the common format. The computer system receives, by the interface module, response data including data (e.g. text data) responsive to the second request. The computer system may implement a conversational artificial intelligence (CAI) platform. The computer system may receive text input from a chat-based input source. The computer system may route another request based on the text input to the AI core, the request being specified in the common text format.